A computer system that performs data stream processing generally handles data that arrives from one moment to the next (hereinafter, may be referred to as time-series data). Time-series data is one element comprising stream data. In other words, stream data is an aggregation of time-series data.
Time-series data comprises a timestamp, which is information denoting the time that this time-series data occurred. In data stream processing, operations (for example, grouping, duplication removal, sum/difference/product set operations, a tabulation operation, and a join operation) are performed on time-series data.
However, since time-series data arrives endlessly, the stream data (large quantity of time-series data) must be separated into finite datasets. As a method for separating the stream data into finite datasets, for example, there is the sliding window method (for example, Non Patent Literature 1).
According to the sliding window method, the lifetime of the time-series data is configured. Data stream processing, for example, includes the following processes:    (1) A process for acquiring on the basis of the configured lifetime a window (a dataset) 42, which at a certain point in time will become an operation target, from inputted stream data 41 as shown in FIG. 2;    (2) a process for performing an operation on the time-series data (input data) 45 included in the dataset 42; and    (3) a process for sequentially outputting output data 46 inside a dataset 43 of output data 46 comprising the operation result.
As a result of this, stream data 44 is constructed using the sequentially outputted output data 46.